Albert Rizvanov (Ph.D., Dr. Sci.) graduated from Kazan State University, Russia (biology, microbiology) in 1996. After completing his Ph.D. (2003) in cellular and molecular biology at the University of Nevada, Reno, USA, he undertook his Dr. Sci. (2011) in biochemistry (Habilitation) at Kazan Federal University (KFU), Russia. Currently, Albert Rizvanov is a Professor and Director of the Center for Precision and Regenerative Medicine, Institute of Fundamental Medicine and Biology, KFU. He is the head of the Open Lab Gene and Cell Technologies Laboratory, Director of the Department of Exploratory Researches of Pharmaceutical Research and Education Center and head of the Center of Excellence "Regenerative Medicine". Additionally, he is the Vice-Director of the Strategic Academic Unit "Translational 7P Medicine" as part of the government program of competitive growth ("5-100 Program") and the corresponding member of the Tatarstan Academy of Sciences, Russian Federation. Albert is an authoron more than 300 peer-reviewed journal articles, 3 book chapters, and 22 patents, he has successfully supervised 15 Ph.D. and 2 Dr.Sci. dissertations, and is the head of the biochemistry, microbiology, and genetics dissertation committee at KFU. He is the principal investigator of more than 50 grants supported by NATO, British Council, Russian Science Foundation, Russian Foundation for Basic Research and other Russian government federal programs and industry contracts. His fields of expertise include regenerative medicine, precision medicine, gene and cell therapy, molecular neurobiology, molecular virology, cancer diagnostics and therapy. In 2019 Albert Rizvanov became an Honorary Professor of Fundamental Medicine at the Faculty of Medicine and Health Sciences, University of Nottingham, UK. Bikesh Kumar Singh (Ph. D.) is Assistant Professor in Department of Biomedical Engineering at National Institute of Technology Raipur, Raipur (Chhattisgarh) India. He obtained his B.E. (Electronics and Telecommunication Engineering) Gold Medalist and M. Tech. (Electronics and Telecommunication Engineering) Honors from Pt. Ravishankar Shukla University, Raipur. He received his Ph.D. in Biomedical Engineering from National Institute of Technology Raipur, Raipur (Chhattisgarh) India. He has published more than 70 research papers in various international and national journals and conferences. He is active reviewer and has reviewed several research articles of reputed International Journals. He has teaching and research experience of 12 years. He has been Head of the Department of Department of Biomedical Engineering for 5 years. He is member of International Professional Societies such as IEEE (Senior member) & IACSIT and also of many National Professional bodies like CSI India, IETE India, ISCA India and IEI India. He has received several awards like Chhattisgarh Young Scientist Award, IETE Gowri Memorial Award, IEI Young Engineer Award. He has delivered several expert talks in the area of Machine Learning Applications. He has organized several workshops and international conference in area of Biomedical Engineering, Machine Learning and Softcomputing. His research interest includes applications of machine learning and artificial intelligence in medical image analysis, biomedical signal analysis, computer aided diagnosis, computer vision and cognitive science. Padma Ganasala (Ph. D.) is currently working as Associate Professor in the Department of Electronics and Communications Engineering, Gayatri Vidya Parishad College of Engineering, Visakhapatnam, India. She had received her Ph.D. in Medical Image Fusion from Indian Institute of Technology Roorkee (IIT-ROORKEE). She is a recipient of MHRD fellowship during studies. To her credit, she possesses several publications in reputed international journals and conferences. She has reviewed many journal papers published by prestigious journals and conferences. She is a Life Member of ISTE. Her research interests include medical image processing and analysis, biomedical signal processing, machine learning and deep learning.
